The modern communications era has brought about a tremendous expansion of wireline and wireless networks. Computer networks, television networks, and telephony networks are experiencing an unprecedented technological expansion, fueled by consumer demand. Wireless and mobile networking technologies have addressed related consumer demands, while providing more flexibility and immediacy of information transfer.
Current and future networking technologies continue to facilitate ease of information transfer and convenience to users. One area in which there is a demand to increase ease of information transfer relates to the delivery of services to a user of a mobile terminal. The services may be in the form of a particular media or communication application desired by the user, such as a music player, a game player, an electronic book, short messages, email, etc. The services may also be in the form of interactive applications in which the user may respond to a network device in order to perform a task or achieve a goal. The services may be provided from a network server or other network device, or even from the mobile terminal such as, for example, a mobile telephone, a mobile television, a mobile gaming system, etc.
In many applications, it is necessary for the user to receive audio information such as oral feedback or instructions from the network. An example of such an application may be paying a bill, ordering a program, receiving driving instructions, etc. Furthermore, in some services, such as audio books, for example, the application is based almost entirely on receiving audio information. It is becoming more common for such audio information to be provided by computer generated voices. Accordingly, the user's experience in using such applications will largely depend on the quality and naturalness of the computer generated voice. As a result, much research and development has gone into speech processing techniques in an effort to improve the quality and naturalness of computer generated voices.
An example of speech processing includes voice conversion related applications in which the identity of a speaker may be changed. However, in order to train conversion models for performing this type of speech processing, it is typical for relatively large sets of training data comprising parallel sentences or utterances to be required, which can be undesirable since it may lead to increases in memory requirements and the recording of large training sets may be inconvenient and time-consuming for the users. Additionally, current techniques often suffer from over-smoothing and/or discontinuity problems.
Particularly in mobile environments, increases in memory consumption directly affect the cost of devices employing such methods. However, even in non-mobile environments, the possible increases in application footprints and memory consumption may not be desirable. Thus, a need exists for providing a mechanism for increasing the efficiency of voice conversion applications, while not sacrificing quality and accuracy.